AbstractÐWe propose a method for automatically classifying facial images based on labeled elastic graph matching, a 2D Gabor wavelet representation, and linear discriminant analysis. Results of tests with three image sets are presented for the classification of sex, ªrace,º and expression. A visual interpretation of the discriminant vectors is provided.
Evolutionary computation is used to construct undetectable computer attack scripts. Using a simulated operating system, we show that scripts can be evolved to cover their tracks and become difficult to detect from log file analysis.
Interactive evolutionary design, a powerful technique where one marries the exploratory capabilities of evolutionary computation with the aesthetic skills and domain knowledge of the human as selective agent, has been demonstrated to be an extremely powerful exploratory design method. One of interactive evolutions most promising uses is in discovering individual-level rules of behavior and interaction that will produce a desired collective pattern in a group of human or non-human agents. The problem of finding micro-rules that produce interesting macro-behavior poses significant challenges, all the more so when what constitutes "interesting" macro-behavior may not be known ahead of time. Here, the system at our disposal is a real-time crowd polling and display system, whose potential for generating interesting group behavior remains largely untapped.Additionally, because the system is capable of polling large crowds of people in real time, it presents an ideal framework within which to take advantage of a relatively unexplored form of interactive evolution -collective evolution, where the opinions of the entire group are taken into account in the design of the next generation. Collective evolution has a broad range of potential applications, including marketing research and logo and brand name design.
Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information.
ABSTRACTWe have developed a realistic agent-based simulation model of hacker behavior. In the model, hacker scripts are generated using a simple but powerful "hacker grammar" that has the potential to cover all possible hacker scripts. The model can be used to characterize the evidence generated by any hacker script, including new scripts that appear every day, and to train inexperienced investigators and incident handlers how to deal with a compromised system and look for evidence. The model can also be used in order to design sophisticated artificial intelligence techniques to automate intrusion detection and evidence collection. Finally, we summarize an extension of this work in which an evolutionary algorithm was used to evolve scripts that achieve certain goals without being detected.
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